Population response in autaptic and disynaptic inhibitory cortical microcircuitry: a computational study

The dynamic of the neuronal response is highly influenced by the nature and the amount of noise that a neuron experienced, this noise being either external and internal to the neuron. The 10 000 synaptic afferents may not fire synchronously, hence, cause the synaptic noise, and ion channels are intrinsically noisy. Recent discoveries at the cellular- and microcircuit-levels describing several stereotypical pathways were reported, such as target-specific homosynaptic plasticity, disynaptic inhibition between pyramidal neurons, and differential self-inhibition of interneurons. This study proposes a large exploration of neuronal dynamical response properties, and describes how they are affected by these features. In particular, the introduction of heterogeneity in the dynamic of sodium channels affects the filtering behavior of the neuronal response, whereas inhibitory autapses seem to have less impact on the dynamic. However, autapses may have an important role for enhancing the spike time reliability by reducing the intrinsic channel noise, allowing the timing precision relevant to several behaviors, as suggest simulations of simple microcircuitry architectures and neocortical slice recordings

Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition

Ayah Khubieh

The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding.

Oxford Univ Press Inc2015

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

Simple Neuron Models and Adaptation

Skander Mensi

2008